CN113269673A - Three-dimensional point cloud splicing method based on standard ball frame - Google Patents
Three-dimensional point cloud splicing method based on standard ball frame Download PDFInfo
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Abstract
The invention discloses a three-dimensional point cloud splicing method based on a standard ball frame, which is characterized in that the standard ball frame is used as an intermediate medium, so that the unification of local coordinate systems in a measurement view field area is realized, and accumulated errors caused by multi-view-field splicing are eliminated; meanwhile, in the accurate point cloud registration process, a virtual overlapping area is constructed by utilizing the spherical point cloud with characteristic constraint, more accurate corresponding point pairs are provided, and the conversion relation between the spliced point clouds is optimized and solved by utilizing a weight function in combination with an improved iteration closest point algorithm, so that the larger noise influence in the point clouds is reduced, and the high-precision three-dimensional point cloud splicing is realized.
Description
Technical Field
The invention belongs to the field of three-dimensional point cloud splicing, and particularly relates to a three-dimensional point cloud splicing method based on a standard ball frame.
Background
With the rapid development of science and technology and industry, the demands for rapid three-dimensional shape measurement of entities are promoted under a series of requirements of reverse engineering, product digital detection, shape 3D mapping and the like. With the continuous development of the three-dimensional measurement technology, the three-dimensional measurement technology is more and more widely applied to the fields of aerospace, automobile industry and complex free-form surface mold forming, the requirements of people on the measurement precision, the measurement speed and the measurement view field range of the three-dimensional shape are higher and higher, and the three-dimensional measurement technology with high precision and large view field becomes a research hotspot and difficulty at present.
No matter which mode is adopted in the three-dimensional profile measurement of object, owing to receive the restriction of testee structure, measuring instrument working range's restriction, often can't realize the measurement to the whole three-dimensional profile of object through once measuring, need carry out diversified multi-angle measurement to the testee, in order to obtain the exact whole external morphology of object, just need pass through a point cloud concatenation technique, splice the single field data of measuring together and obtain the whole data point cloud on testee surface, the data processing stage of point cloud concatenation among the three-dimensional measurement process simultaneously, the concatenation precision between each local point cloud data is also directly influencing holistic measurement accuracy. Therefore, the point cloud splicing technology is a key process in three-dimensional topography measurement and is also an important processing step for realizing the reconstruction of the complete three-dimensional outline of the object.
Disclosure of Invention
The invention aims to overcome the defects and provides a three-dimensional point cloud splicing method based on a standard ball frame, wherein the standard ball frame is used as an intermediate medium, a plurality of single-field measurement data are converted into a unified coordinate system, and the accumulated error of multi-view-field splicing is avoided; by means of constructing the characteristic spherical surface, the overlapping degree of the point cloud data of each measurement point is improved, the splicing error is reduced, and efficient and high-precision three-dimensional point cloud splicing is realized.
In order to achieve the above object, the present invention comprises the steps of:
s1, fixing the spatial position of the measuring equipment combination to enable at least three standard balls to be shot in the measuring field of view of the measuring equipment;
s2, measuring the point cloud data of each standard ball on the ball frame by the measuring equipment, fitting the ball surface to obtain the center coordinates of each standard ball, and establishing a ball frame coordinate system;
s3, calculating the curvature of the standard spherical point cloud, and extracting the point cloud of the standard spherical surface;
s4, segmenting different spherical surfaces through distance constraint, establishing a sphere center distance matrix, and establishing corresponding relations of different spheres;
s5, aligning the centers of the spheres according to the corresponding relation of different spheres to complete the splicing of corresponding point clouds from the same standard sphere;
and S6, constructing a continuous spherical surface by utilizing the fitted spherical center coordinates and the known radius characteristics, and finishing accurate splicing of the spherical point cloud by an iterative closest point algorithm.
The measuring equipment is a structured light measuring head or a three-coordinate measuring machine.
In S4, after the different spherical surfaces are segmented, the corresponding relationship between the spherical surfaces from the same standard sphere in the two point clouds is determined.
Finding a corresponding relation by establishing a sphere center distance matrix, wherein the sphere center distance matrix M is as follows:
each element in the matrix M is the distance between two sphere centers, and if a certain row of the matrix M and a certain row of the other point cloud sphere center distance matrix have at least three equal elements, two spheres corresponding to the reorganization data come from the same standard sphere.
In S6, the center of the sphere is calculated by adopting a method of fitting the measurement points of any part of the sphere, the radius of the sphere is measured as a constraint condition to improve the construction accuracy, and then the whole sphere is constructed according to the center and the radius of the sphere, wherein the spherical equation is as follows:
(xsi-x0)2+(ysi-y0)2+(zsi-z0)2＝r2
in the formula: x is the number ofsi,ysi,zsiAs coordinates of points on the sphere, x0,y0,z0Is the coordinate of the center of the sphere, and r is the radius of the sphere;
pre-measuring the spherical target radius, setting the spherical radius as a characteristic constraint as a known parameter, and solving x by minimizing the following function0,y0,z0；
r＝constant
In the formula: n is the number of fitting points;
and constructing a complete sphere by using any pair of sphere centers and radii, wherein the constructed surface has an overlapping region with other measuring point clouds.
In S6, the specific method for completing accurate registration of the spherical point clouds by the iterative closest point algorithm is as follows:
solving the rotation matrix R and the translational vector T by using the solved corresponding point pairs through a quaternion method, wherein an objective function is as follows:
alternatively, the solution is solved by minimizing the following equation:
in the formula: q. q.siFor the ith stitching point in the stitched point cloud,
for the points to which the virtual overlap region corresponds, njThe number of registration points in each point cloud, m is the number of targets, N is the total number of all registration points, | | | |, is the Euclidean norm, ρjIs the weight of the jth point cloud, ρjIs at a value of [0,1 ]]；
In the formula: u. ofjThe deviation between the target fitted radius and its actual radius for the jth point cloud.
Compared with the prior art, the system realizes the unification of local coordinate systems in a measurement view field area by taking a standard ball frame as an intermediate medium, and eliminates accumulated errors caused by multi-view-field splicing; meanwhile, in the accurate point cloud registration process, a virtual overlapping area is constructed by utilizing the spherical point cloud with characteristic constraint, more accurate corresponding point pairs are provided, and the conversion relation between the spliced point clouds is optimized and solved by utilizing a weight function in combination with an improved iteration closest point algorithm, so that the larger noise influence in the point clouds is reduced, and the high-precision three-dimensional point cloud splicing is realized.
Drawings
FIG. 1 is a simplified schematic of the present invention;
FIG. 2 is a flow chart of a three-dimensional point cloud stitching method of the present invention;
FIG. 3 is a diagram illustrating the stitching result in an embodiment, wherein (a) is theoretical data and (b) is stitched data;
wherein, 1a is a first structured light measuring head, 1b is a second structured light measuring head, and 2 is a standard ball rack.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention comprises the following steps:
fixing the spatial position of a measuring equipment combination and ensuring that the relative position of the measuring equipment combination is unchanged in the measuring process; the number of the measuring devices is two or more, and the measuring devices can be freely selected according to the measuring space range; the measuring equipment is not limited to a structured light measuring head, and can also be a three-coordinate measuring machine or other measuring equipment, and meanwhile, a standard ball frame adapting to a field range is required to be selected to complete point cloud splicing.
Measuring the point cloud data of each standard ball on the ball frame through high-precision equipment (such as a three-coordinate machine), and fitting the spherical surface to obtain the center coordinates of each standard ball so as to establish a ball frame coordinate system; a ball frame coordinate system is established in a high-precision measurement mode, and the coordinate system is used as an intermediate medium to realize data unification of different coordinate systems.
Thirdly, three or more than three standard spheres are shot in the measurement field corresponding to each measurement device, and the spherical point cloud is extracted by calculating the curvature of the point cloud;
step four, segmenting different spherical surfaces through distance constraint, and establishing a sphere center distance matrix for establishing corresponding relations of different spheres; after the different spherical surfaces are segmented, the corresponding relation of the spherical surfaces from the same standard sphere in the two point clouds is determined. Finding a corresponding relation by establishing a sphere center distance matrix, wherein the sphere center distance matrix M is as follows:
each element in the matrix M is the distance between two centers of sphere, and if a row of the M matrix and a row of another point cloud center distance matrix have three or more equal elements, the two corresponding spheres are from the same standard sphere.
Step five, roughly registering the centers of the spherical surfaces, and completing the splicing of corresponding point clouds from the same standard sphere by aligning the centers of the fitted spherical bodies;
and step six, constructing a continuous sphere by utilizing the sphere center fitting coordinates and the known radius characteristics, and finishing accurate splicing of the spherical point cloud through an improved Iterative Closest Point (ICP) algorithm.
The purpose of fitting the continuous spherical surface is to construct a virtual overlapping area, the center of the spherical surface is calculated by adopting a method of fitting any part of measurement points of the spherical surface, the radius of the spherical surface is measured at the same time to serve as a constraint condition to improve the construction precision, then the whole spherical surface is constructed according to the center and the radius of the spherical surface, and the spherical equation is as follows:
(xsi-x0)2+(ysi-y0)2+(zsi-z0)2＝r2
in the formula: (x)si,ysi,zsi) -coordinates of points on the ball; x is the number of0,y0,z0-as centre of sphere coordinates; r-radius of the sphere.
Pre-measuring the spherical target radius, setting the spherical radius as a characteristic constraint as a known parameter, and solving x by minimizing the following function0,y0,z0:
r＝constant
In the formula: n is the number of fitting points.
The minimization equation is a non-linear problem and is solved using the Levenberg-Marquardt algorithm. Then, a complete sphere is constructed by using any pair of sphere centers and radii, and the constructed surface has an overlapping area with other measuring point clouds.
Accurate splicing of spherical point cloud is completed through an improved Iterative Closest Point (ICP) algorithm, the rotation matrix R and the translation vector T are solved through a quaternion method by utilizing the solved corresponding point pairs, and the objective function is as follows:
alternatively, the solution can be solved by minimizing the following equation:
in the formula: q. q.si-stitching the ith stitching point in the point cloud;
-the point to which the virtual overlap region corresponds; n isj-registering the number of points for each point cloud; m is the target number; n-the total number of all registration points; | | · | — european norm; rhoj-the weight of the jth point cloud, whose value is at [0,1 ]]Mainly depends on the measurement accuracy of the stitched point cloud.
In the formula: u. ofj-deviation between the target fitting radius of the jth point cloud and its actual radius. The weight ρjMainly used for improving the stability of splicing, and the influence of some larger errors in the spliced point cloud can be reduced by a small weight.
As shown in fig. 1, the spatial positions of the first structured light measuring head 1a and the second structured light measuring head 1b are fixed, the spatial positions are guaranteed to be unchanged in the measuring process, and a standard ball rack 2 with a proper structure and size is selected according to the field range.
And (3) placing the standard ball frame on a three-coordinate machine to carry out contact type high-precision measurement, fitting the sphere center coordinate of each standard ball, and fixing the position of the ball frame in the measurement process to ensure the accuracy of establishing a ball frame coordinate system.
The first structured light measuring head 1a and the second structured light measuring head 1b project preset patterns to the standard ball frame 2, three-dimensional point cloud data are obtained, and preprocessing such as point cloud denoising is conducted.
Subsequently, the measured point clouds of the two fields of view are subjected to a point cloud stitching process as shown in fig. 2.
a) Identifying the spherical point cloud in the actually measured point cloud: extracting spherical point cloud through curvature characteristics;
b) dividing the actually measured spherical point cloud into different spheres: randomly extracting a point q from the spherical point cloudiUsing point qiThe sphere center position is calculated by fitting a sphere with the neighborhood point cloud of the three standard spheres, and the three standard spheres are separated;
c) identifying different standard balls: determining a corresponding relation from the same standard ball in the theoretical spherical point cloud and the actually measured spherical point cloud by establishing a spherical center matrix;
d) roughly aligning the centers of the spheres: and splicing corresponding point clouds from the same standard sphere by aligning the centers of the fitted spheres.
e) Constructing a virtual spherical surface: the sphere radius is set as a constant parameter as a constraint condition to construct a virtual sphere.
f) Selecting a corresponding point pair: the theoretical spherical point cloud P is taken as a fixed point cloud. Actually measuring each point Q in spherical point cloud QiAre respectively connected to corresponding fitting spherical centers Oj(j ═ 1,2, 3). Connecting line and spherical surface Sj(j ═ 1,2,3) at qiAt this point, consider qiThe corresponding point of (2).
And solving the rotation matrix R and the translational vector T by adopting a quaternion method.
Example (b): in order to verify the feasibility of the method, a platform is built, the experiment is carried out according to the steps, and the splicing result is shown in fig. 3. Statistics were performed on the results shown, and the results are shown in tables 1 to 5.
TABLE 1 theoretical centre distance
TABLE 2 center distance after splicing
TABLE 3 center of sphere distance and error in field of view a
TABLE 4 center of sphere distance error in field b
TABLE 5 spherical center distance error of adjacent fields of view
The conclusion is that: the center distance error in each field of view in tables 3 and 4 was taken as the measurement error of the whole system, and the average error of the center distance in each field of view was calculated to evaluate the measurement error, and the average error of the whole system was 0.01906 mm. The spherical center distance errors among different view fields in table 5 are used as the accumulated errors of the splicing precision (including the measurement errors and the splicing errors), the average value of the spherical center distance errors of different view fields is calculated to evaluate the accumulated errors of the splicing, and the average splicing accumulated errors of the whole system are as follows: 0.043347 mm. Therefore, the splicing precision of the experiment can be verified to be 0.024287 mm.
Claims (6)
1. A three-dimensional point cloud splicing method based on a standard ball frame is characterized by comprising the following steps:
s1, fixing the spatial position of the measuring equipment combination to enable at least three standard balls to be shot in the measuring field of view of the measuring equipment;
s2, measuring the point cloud data of each standard ball on the ball frame by the measuring equipment, fitting the ball surface to obtain the center coordinates of each standard ball, and establishing a ball frame coordinate system;
s3, calculating the curvature of the standard spherical point cloud, and extracting the point cloud of the standard spherical surface;
s4, segmenting different spherical surfaces through distance constraint, establishing a sphere center distance matrix, and establishing corresponding relations of different spheres;
s5, aligning the centers of the spheres according to the corresponding relation of different spheres to complete the splicing of corresponding point clouds from the same standard sphere;
and S6, constructing a continuous spherical surface by utilizing the fitted spherical center coordinates and the known radius characteristics, and finishing accurate splicing of the spherical point cloud by an iterative closest point algorithm.
2. The three-dimensional point cloud splicing method based on the standard ball frame as claimed in claim 1, wherein the measuring equipment is a structured light measuring head or a three-coordinate measuring machine.
3. The method as claimed in claim 1, wherein in S4, after the different spherical surfaces are segmented, the corresponding relationship between the spherical surfaces of the two point clouds from the same standard spherical surface is determined.
4. The method for three-dimensional point cloud stitching based on the standard ball frame as claimed in claim 1, wherein the correspondence is found by establishing a sphere center distance matrix, wherein the sphere center distance matrix M:
each element in the matrix M is the distance between two sphere centers, and if a certain row of the matrix M and a certain row of the other point cloud sphere center distance matrix have at least three equal elements, two spheres corresponding to the reorganization data come from the same standard sphere.
5. The method for three-dimensional point cloud stitching based on the standard ball rack as claimed in claim 1, wherein in S6, the center of the sphere is calculated by a method of fitting the measurement points of any part of the sphere, the radius of the sphere is measured as a constraint condition to improve the construction accuracy, and then the whole sphere is constructed according to the center and the radius of the sphere, and the spherical equation is as follows:
(xsi-x0)2+(ysi-y0)2+(zsi-z0)2＝r2
in the formula: x is the number ofsi,ysi,zsiAs coordinates of points on the sphere, x0,y0,z0Is the coordinate of the center of the sphere, and r is the radius of the sphere;
pre-measuring the spherical target radius, setting the spherical radius as a characteristic constraint as a known parameter, and solving x by minimizing the following function0,y0,z0；
r＝constant
In the formula: n is the number of fitting points;
and constructing a complete sphere by using any pair of sphere centers and radii, wherein the constructed surface has an overlapping region with other measuring point clouds.
6. The method for splicing the three-dimensional point clouds based on the standard ball frames as claimed in claim 1, wherein in S6, the specific method for completing the accurate splicing of the spherical point clouds by the iterative closest point algorithm is as follows:
solving the rotation matrix R and the translational vector T by using the solved corresponding point pairs through a quaternion method, wherein an objective function is as follows:
alternatively, the solution is solved by minimizing the following equation:
in the formula: q. q.siFor the ith stitching point in the stitched point cloud,
for the points to which the virtual overlap region corresponds, njThe number of registration points in each point cloud, m is the number of targets, N is the total number of all registration points, | | | |, is the Euclidean norm, ρjIs the weight of the jth point cloud, ρjIs at a value of [0,1 ]]；
In the formula: u. ofjAs the target of the jth point cloudDeviation between the fitted radius and its actual radius.
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| CN115795579A (en) * | 2022-12-23 | 2023-03-14 | 岭南师范学院 | Rapid coordinate alignment method for featureless complex surface error analysis |
| CN115795579B (en) * | 2022-12-23 | 2023-06-27 | 岭南师范学院 | A Fast Coordinate Alignment Method for Measurement Error Analysis of Featureless Complex Surfaces |
| CN116758240A (en) * | 2023-06-02 | 2023-09-15 | 中交四航工程研究院有限公司 | A spherical surface fitting method for grid structure point cloud |
| CN116721239A (en) * | 2023-06-12 | 2023-09-08 | 山西阳光三极科技股份有限公司 | Automatic point cloud splicing method based on multiple radar devices |
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